Update README.md

README.md

@@ -213,6 +213,15 @@ xtuner convert pth_to_hf ./llava_internlm2_chat_7b_dinov2_e1_gpu8_finetune.py ./
 The adapter still need to be used with the internlm/internlm2-chat-7b and facebook/dinov2-large models. I have not tried to merge them yet but it is possible with Xtuner, see this [tutorial](https://github.com/InternLM/xtuner/blob/f63859b3d0cb39cbac709e3850f3fe01de1023aa/xtuner/configs/llava/README.md#L4).
 
 ## MMBench Evaluation
+You can first download the MMBench data:
+```
+wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN.tsv
+wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN.tsv
+wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN.tsv
+wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN.tsv
+wget https://opencompass.openxlab.space/utils/VLMEval/CCBench.tsv
+```
+Then run:
 ```
 NPROC_PER_NODE=8 xtuner mmbench internlm/internlm2-chat-7b \
 --visual-encoder facebook/dinov2-large \
@@ -221,6 +230,7 @@ NPROC_PER_NODE=8 xtuner mmbench internlm/internlm2-chat-7b \
 --data-path $MMBENCH_DATA_PATH \
 --work-dir $RESULT_PATH
 ```
+You can also use [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) to evaluate it on other benckmarks.
 
 ## Deployment
 Xtuner team is developing HF chatbot (based on Huggingface transformers) and LMDeploy chatbot (based on TurboMind). I am waiting for their final version of API.